
<img src="images/plot_margin.PNG"/>
reference: www.r-graph-gallery.com
# Histogram with 5 breakpoints
hist(iris$Sepal.Length, breaks=10)m <- matrix( c(4,6,4,6,4,6), nrow=2 )
# [,1] [,2] [,3]
# [1,] 4 4 4
# [2,] 6 6 6
barplot(m, legend=rownames(m), beside=TRUE)# Sorting a boxplot based on median value
bymedian <- with(InsectSprays, reorder(spray, -count, median))
boxplot(count ~ bymedian, data = InsectSprays,
xlab = "spray", ylab = "count", varwidth=TRUE,
col = "lightgray")# both 'density' and rug plot
x <- rexp(200)
plot(density(x))
rug(x)x <- rnorm(100)
y <- rexp(100)
qqplot(x, y)http://www.sthda.com/english/wiki/visualize-correlation-matrix-using-correlogram
library(corrplot)
corrplot(cor(mtcars), type="lower")https://rpubs.com/gaston/dendrograms
http://www.sigmath.es.osaka-u.ac.jp/shimo-lab/prog/pvclust/
hc <- hclust(dist(USArrests[1:7,]), "ave")
#--------- plot
suppressMessages(library(dendextend))
dend1 <- as.dendrogram(hc)
cutree(dend1,h=70) # it now works on a dendrogram with dendextend library## Alabama Alaska Arizona Arkansas California Colorado
## 1 1 1 2 1 2
## Connecticut
## 3
dend1 <- color_branches(dend1, k = 4)
dend1 <- color_labels(dend1, k = 5)
plot(dend1)#--------- plot
clus5 = cutree(hc, 5)
plot(hc, xlab = ""
, hang = -1 # set label height
)
rect.hclust(hc, h=70, border="blue")
rect.hclust(hc, h=30, border="red")
legend("topright", "(x,y)"
, c("Strong relation - red", "Good relation - blue") # add text
, text.col=c("red", "blue")
, lty=-1 , pch=NA # set lyt and pch to remove the legend symbols
)# multiple plots in one graph. e.g. 2 rows, 2 columns
par(mfrow=c(2,2),
mar=c(3,1,3,1), oma=c(0,0,0,0) # margin: buttom left, top, right
)
# switch between plots
p <- par('mfg')) # get the plot
par(mfg = p) # focus the plotx<- 1:10
y1 <- sample(seq(1:50), 10)
y2 <- sample(seq(100:200), 10)
# all sides have 2 lines of space margin to give space for axis on the right
par(oma=c(2,2,2,2))
# first plot
plot(x=x, y=y1, type="l", col="red")
# tell R to draw over the first plot
par(new=T)
# do second plot
plot(x=x, y=y2, type ="l", ylab="", yaxt='n' )
axis(4, (range(y2)), ylab='y2') # draw second axis on the right
mtext("y2", side=4, line=2.3, adj=0.5, cex=1) # add test label on the rightlas: 0: always parallel to the axis [default], 1: always horizontal, 2: always perpendicular to the axis, 3: always vertical.
plot(iris$Sepal.Length, las=2)df <- iris[sample(nrow(iris), 30), ]
plot(df$Petal.Width, xaxt="n", xlab = "" )
axis(1, at=1:nrow(df), labels=df$Species, las=2, cex.axis=0.7)y <- runif(10, 1, 5)
x <- seq_along(y)
y2 <- rep(y, each=2)[-(length(y)*2)]
x2 <- rep(x, each=2)[-1]
x3 <- c(min(x2), x2, max(x2))
y3 <- c(0, y2, 0)
plot(x, y, pch=16)
polygon(x3, y3, border=NA, col="grey60")
lines(x2, y2)# create a color pattern
cl <- rainbow(5)
# transparent color
library(scales)
col <- alpha("yellow", 0.4)
# use RColorBrewer library
# http://moderndata.plot.ly/create-colorful-graphs-in-r-with-rcolorbrewer-and-plotly/
RColorBrewer::brewer.pal(3, "Set2")legend("topright", "(x,y)"
, "info text" , title = "title text" # add text
, lty=-1 , pch=NA # set lyt and pch to remove the legend symbols
)library(highcharter)
library(magrittr)
library(xts)
x <- rnorm(100, mean=0)
ts <- ts(x)
fXts <- as.xts(ts)
highchart() %>%
hc_add_series_xts(fXts, id = "usdjpy") # add x axis
highchart() %>%
hc_xAxis(categories = as.character(index(fXts))) %>%
hc_add_series(name = "sss", data = as.numeric(fXts))library(plotly)
x <- c(1:100)
random_y <- rnorm(100, mean=0)
data <- data.frame(x, random_y)
plot_ly(data, x = ~x, y = ~random_y, type='scatter', mode='lines')Producing Simple Graphs with R : http://www.harding.edu/fmccown/r/
http://personality-project.org/r/r.plottingdates.html
Hmisc http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=Hmisc:Ecdf
heatmap-or-plot-for-a-correlation-matrix http://stackoverflow.com/questions/15887212/heatmap-or-plot-for-a-correlation-matrix